






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def __init__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Dict=None       ,			UpperCAmelCase_							:						Dict=True       ,			UpperCAmelCase_							:						List[Any]=None       ,			**UpperCAmelCase_							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     parent
       SCREAMING_SNAKE_CASE							:   Dict						    =     config_class
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     has_text_modality
       SCREAMING_SNAKE_CASE							:   List[str]						    =     kwargs
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     common_properties


     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.config_class(**self.inputs_dict   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     (
           ["hidden_size", "num_attention_heads", "num_hidden_layers"]
           if self.common_properties is None
           else self.common_properties
       )

       # Add common fields for text models
       if self.has_text_modality:
         common_properties.extend(["vocab_size"]   )

       # Test that config has the common properties as getters
       for prop in common_properties:
         self.parent.assertTrue(hasattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			msg=f'''`{prop}` does not exist'''   )

       # Test that config has the common properties as setter
       for idx, name in enumerate(UpperCAmelCase_   ):
         try:
           setattr(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
           self.parent.assertEqual(
               getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			UpperCAmelCase_       ,			msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )}'''   )
         except NotImplementedError:
           # Some models might not be able to implement setters for common_properties
           # In that case, a NotImplementedError is raised
           pass

        # Test if config class can be called with Config(prop_name=..)
       for idx, name in enumerate(UpperCAmelCase_   ):
         try:
           SCREAMING_SNAKE_CASE							:   List[str]						    =     self.config_class(**{name: idx}   )
           self.parent.assertEqual(
               getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			UpperCAmelCase_       ,			msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )}'''   )
         except NotImplementedError:
           # Some models might not be able to implement setters for common_properties
           # In that case, a NotImplementedError is raised
           pass


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class(**self.inputs_dict   )
       SCREAMING_SNAKE_CASE							:   Any						    =     json.loads(config.to_json_string()   )
       for key, value in self.inputs_dict.items():
         self.parent.assertEqual(obj[key]       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.config_class(**self.inputs_dict   )

       with tempfile.TemporaryDirectory() as tmpdirname:
         SCREAMING_SNAKE_CASE							:   Dict						    =     os.path.join(UpperCAmelCase_       ,			"config.json"   )
         config_first.to_json_file(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     self.config_class.from_json_file(UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.config_class(**self.inputs_dict   )

       with tempfile.TemporaryDirectory() as tmpdirname:
         config_first.save_pretrained(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class.from_pretrained(UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.config_class(**self.inputs_dict   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     "test"
       with tempfile.TemporaryDirectory() as tmpdirname:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     os.path.join(UpperCAmelCase_       ,			UpperCAmelCase_   )
         config_first.save_pretrained(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.config_class.from_pretrained(UpperCAmelCase_       ,			subfolder=UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.config_class(**self.inputs_dict       ,			num_labels=5   )
       self.parent.assertEqual(len(config.idalabel   )       ,			5   )
       self.parent.assertEqual(len(config.labelaid   )       ,			5   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     3
       self.parent.assertEqual(len(config.idalabel   )       ,			3   )
       self.parent.assertEqual(len(config.labelaid   )       ,			3   )


     def 					_A							(					self							:						str   ):
       if self.config_class.is_composition:
         return
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class()
       self.parent.assertIsNotNone(UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     copy.deepcopy(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     self.config_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     []
       for key, value in config_common_kwargs.items():
         if key == "torch_dtype":
           if not is_torch_available():
             continue
           else:
             import torch

             if config.torch_dtype != torch.floataa:
               wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)   )
         elif getattr(UpperCAmelCase_       ,			UpperCAmelCase_   ) != value:
           wrong_values.append((key, getattr(UpperCAmelCase_       ,			UpperCAmelCase_   ), value)   )

       if len(UpperCAmelCase_   ) > 0:
         SCREAMING_SNAKE_CASE							:   Any						    =     "\n".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]   )
         raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''   )






     def 					_A							(					self							:						Tuple   ):
       self.create_and_test_config_common_properties()
       self.create_and_test_config_to_json_string()
       self.create_and_test_config_to_json_file()
       self.create_and_test_config_from_and_save_pretrained()
       self.create_and_test_config_from_and_save_pretrained_subfolder()
       self.create_and_test_config_with_num_labels()
       self.check_config_can_be_init_without_params()
       self.check_config_arguments_init()







import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_SCREAMING_SNAKE_CASE 		=      """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

_SCREAMING_SNAKE_CASE 		=      """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

_SCREAMING_SNAKE_CASE 		=      """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION		)
class SCREAMING_SNAKE_CASE_		(      datasets.Metric		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				)   ,   id="""references"""				),
            }				)   ,   codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,   reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]=4   ,   lowerCamelCase_						:						List[Any]=False				):





    """simple docstring"""


    UpperCamelCase			  =      compute_bleu(
        reference_corpus=lowerCamelCase_   ,   translation_corpus=lowerCamelCase_   ,   max_order=lowerCamelCase_   ,   smooth=lowerCamelCase_				)
    ((UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase))			  =      score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
